1. Field of the Invention
The present invention relates to a knowledge acquisition system for efficiently modifying the knowledge base prepared in developing a diagnostic expert system, by using the knowledge acquired in the actual cases diagnosed by means of the diagnostic expert system.
2. Description of the Related Art
Recently, thanks to the advance in computer technology, so-called expert systems have been developed and put to practical use, in which a computer performs part of the work only experts can accomplish. An expert system is developed and verified in the following process. First, a knowledge base is prepared by compiling a multitude of knowledge pieces which human experts have acquired through their experience. Next, a computer diagnoses several cases based on the knowledge base, and an expert diagnoses the same cases. Then, the computer diagnosis is compared with the diagnosis made by human expert, thereby finding differences between the diagnoses. Finally, the knowledge base is modified, thereby canceling the differences.
Hitherto, to collect pieces of knowledge which can be utilized to modify the knowledge base, a person known as "knowledge engineer" must interview human expert and acquire sufficient expert knowledge to modify the knowledge base.
To relieve the knowledge engineer from the tedious task of acquiring a multitude of expert knowledge, it is demanded that a automatic knowledge acquisition system be developed, in which a computer can acquire expert knowledge with high efficiency. To meet this demand, various knowledge acquisition systems have been and are being developed. Knowledge-acquiring techniques are disclosed in Sandra Marcus, Automating Knowledge Acquisition for Expert Systems, Kluwer Academic Publishers, 1988, pp. 37-80.
The knowledge base of a diagnostic expert system includes an associative network defining the relationship among events which may occur in an object of diagnosis. In the expert system of this type, the associative network is traced in the forward direction from the symptom event (e.g., an undesirable one) detected first, thereby obtaining a causal event for the symptom event as the diagnostic result.
In the conventional system, if an error is found in the diagnostic result, diagnostic knowledge is modified in the following way. First, all analysis routes traced to obtain the erroneous result of analysis are presented to a human expert. Then, the human expert points out where an error has occurred in the presented analysis route. Finally, the knowledge applied in obtaining the erroneous result of analysis is modified.
In this method, what serves to find a defect in the knowledge base is nothing but the data about the analysis route in which the error has occurred. In other words, the expert cannot help but rely on this data in order to determine where in that route the error has taken place. Therefore, with the conventional system, it is difficult, even for a human expert, to find a defect in the knowledge base and, hence, to modify the knowledge base to remove this defect.